apparatus and method for the analysis of a process having parameter-based faults

ABSTRACT

An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputter configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputter and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to assess a process status.

RELATED APPLICATION

This application is a Continuation of U.S. patent application Ser. No.11/633,455, filed on Dec. 5, 2006, which claims the benefit of U.S.Provisional Patent Application No. 60/741,894, filed on Dec. 5, 2005,which is herein incorporated in its entirety by reference.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to analyzing a process for the purpose ofcontrolling and reducing the occurrence of faults, and, moreparticularly, to controlling process faults by performing a statisticalanalysis of collected process parameter values.

Many manufacturing processes suffer from parameter-related faults. Aprocess parameter is a measurable and/or controllable aspect of theprocess (such as equipment temperature, pressure, material compositions,etc. . . . ) A parameter-related fault is a failure of the process whichis linked to the values of the process parameters during a time periodprior to the occurrence of a fault, but not necessarily in adeterministic or direct manner. In many manufacturing processes thistime period is quite lengthy, on the order of minutes to hours, so thatconventional feedback control methods are ineffective. In order toprevent or delay such a fault, it is necessary to identify problematicconstellations of parameter values well before there is any evidencethat a fault is likely to occur in the near future.

For example, the paper manufacturing process suffers from paper breakfaults, which are one of the most disruptive and costly problems in thepaper manufacturing industry. Paper breaks result in many hours of lostproduction, process upsets, reduced reliability and significant loss ofrevenue potential. There is a considerable value and high demand formethods to reduce the number of breaks.

Early detection of an impeding paper break would allow operators to takecorrective actions before the break occurs. Prediction models for paperbreaks are difficult to build due to the complexity of the productionprocess and the many variables involved in the process. Most paper millshave sensors and measurements on the production line that generate andstore manufacturing data. However the collected data must be analyzedand evaluated in order to form predictions of the future process status.

One main goal of on-line process assessment is to determine the currentprobability that a process fault will occur within a certain upcomingtime interval. In other words, what is the expected frequency of aparticular failure under the given process conditions?

For illustration, consider a case in which a vehicle tire is not yetpunctured, but factors including the tire's wear-and-tear record, thein-tire pressure and the road conditions make the puncture highlyprobable within the nearest N kilometers. We want to determine theprobability for tire puncture within the next N kilometers under thecurrent conditions. That is, what is the probability of tire puncturefor a specific tire, under a known pressure, and driving with specificroad conditions? Under different conditions, for example on a differentroad, the probability may be different.

An effective fault control system will, in addition to assessing theprobability of an upcoming fault, issue prompts to the process operatorsuggesting how the probability may be reduced (e.g. change temperatures,materials, etc). Even though the failure is eventually inevitable, thesystem helps decrease the probability (i.e. reduce the frequency) ofthis type of failure, thus reducing the consequent losses.

There is thus a widely recognized need for, and it would be highlyadvantageous to have, a process analysis apparatus and method devoid ofthe above limitations.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is providedan apparatus for the analysis of a process having parameter-basedfaults. The apparatus includes: a parameter value inputter configuredfor inputting values of at least one process parameter, a fault detectorconfigured for detecting the occurrence of a fault, a learning filecreator, associated with the parameter value inputter and the faultdetector, and configured for separating the input values into a firstlearning file and a second learning file, and a learning file analyzerassociated with the learning file creator, configured for performing aseparate statistical analysis of the first and second learning files.The first learning file includes input values from a collection periodpreceding each of the detected faults, and the second learning fileincludes input values input outside the collection periods. The analysisservers for assessing a process status.

According to a second aspect of the present invention there is provideda method for the analysis of a process having parameter-based faults.The method includes the steps of: inputting the values of at least oneprocess parameter over time, detecting occurrences of process faults,separating the input values into a first learning file and a secondlearning file, where the first learning file includes input values froma collection period preceding each of the detected faults, and thesecond learning file comprising input values input outside thecollection periods, and performing a separate statistical analysis ofthe first and second learning files, thereby to assess a process statusfor a specified set of parameter values.

The present invention successfully addresses the shortcomings of thepresently known configurations by providing an apparatus and methodwhich analyzes parameter data collected during process operation, andperforms separate statistical analyses of normal and pre-fault parameterdata. The dimensions of the calculations may be reduced by clusteringinterrelated parameters.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the present invention, suitable methods andmaterials are described below. In case of conflict, the patentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Implementation of the method and system of the present inventioninvolves performing or completing selected tasks or steps manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of preferred embodiments of the method andsystem of the present invention, several selected steps could beimplemented by hardware or by software on any operating system of anyfirmware or a combination thereof. For example, as hardware, selectedsteps of the invention could be implemented as a chip or a circuit. Assoftware, selected steps of the invention could be implemented as aplurality of software instructions being executed by a computer usingany suitable operating system. In any case, selected steps of the methodand system of the invention could be described as being performed by adata processor, such as a computing platform for executing a pluralityof instructions.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of thepreferred embodiments of the present invention only, and are presentedin the cause of providing what is believed to be the most useful andreadily understood description of the principles and conceptual aspectsof the invention. In this regard, no attempt is made to show structuraldetails of the invention in more detail than is necessary for afundamental understanding of the invention, the description taken withthe drawings making apparent to those skilled in the art how the severalforms of the invention may be embodied in practice.

In the drawings:

FIG. 1 shows a knowledge tree model of a paper manufacturing process;

FIG. 2 illustrates a timeline for fault occurrences;

FIG. 3 is a simplified block diagram of an apparatus for the analysis ofa process having parameter-based faults, according to a preferredembodiment of the present invention;

FIG. 4 a shows an example of an input value distribution of two processparameters;

FIG. 4 b shows a joint PDF for a cluster of two parameters;

FIGS. 5 a-5 d illustrates a smoothing operation to obtain a smoothlyvarying PDF;

FIG. 6 shows a pair of overlapping PDFs;

FIGS. 7 a-7 c illustrates how a Normal PDF and Fault PDF may be combinedto form a risk score distribution for a pair of parameters;

FIG. 5 a shows a parameter distribution split into sub-ranges;

FIG. 8 b shows a histogram for chalk levels in a paper making process;

FIG. 9 a shows a set of six process parameters;

FIG. 9 b shows a historical risk level distribution for four individualvectors and for the overall set of vectors;

FIG. 10 shows a Normal PDF and a Fault PDF superimposed over a twodimensional risk level array;

FIG. 11 is a simplified block diagram of a system for the monitoring andcontrol of a parameter-based industrial process, according to anexemplary embodiment of the present invention; and

FIG. 12 is a simplified flowchart of a method for the analysis of aprocess having parameter-based faults, in accordance with a preferredembodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present embodiments are of an apparatus and method for the analysisof a process having parameter-based faults, which monitor a complexprocess, and provide indicators when the process is moving intoconditions which increase the likelihood of a fault. Specifically, thepresent embodiments may be used both to alert operators to problematicoperating conditions and to identify parameter values which may reducethe frequency of fault occurrences.

The present embodiments deal with processes where stoppages and/orfailures are known to be inevitable (e.g., paper break during papermanufacturing, tire puncture, etc.). These failures often happen atcertain average rate, which depends on the process conditions. In manycases, especially in complicated production lines, resuming the processafter such failures usually takes considerable time, and the economicloss is high. The present embodiments use collected parameter value datato create patterns of normal process behavior and to identify on-linedeviations from the normal behavior.

The preferred embodiments discussed below:

-   -   Use key process variables and optimal lags    -   Calculate optimal parameter grouping (clusters)

Generate a per cluster probability profile for a fault, based on thecurrent set point

-   -   Assign a risk value to the current set point    -   Provide a risk score for an array of parameter vectors to affect    -   Provide real-time updates to a process operator

The principles and operation of an apparatus and method for analysis ofa process according to the present invention may be better understoodwith reference to the drawings and accompanying descriptions.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not limited in its applicationto the details of construction and the arrangement of the components setforth in the following description or illustrated in the drawings. Theinvention is capable of other embodiments or of being practiced orcarried out in various ways. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting.

The following embodiments are directed at a non-limiting exemplaryembodiment of paper making process. The process fault to be consideredfor the purposes of this example is a Paper Tear (PT). The problem ofpaper breaks (tears) during paper manufacturing is used for the purposesof illustration only. The introduced analysis and control system appliesto any manufacturing or parameter-based process in which faults occur atleast partially from identifiable parameters of the process.

Parameter-based processes may be characterized as having three types ofparameters:

1) Controllable parameters {C} which may be changed by the processoperator (e.g. pipe feeding clearance, vacuum power, etc.)

2) Measurable (uncontrollable) parameters {M} which may be measured orknown, but are not directly controlled by the operator (e.g. period oftime since the last maintenance check; outside air humidity etc.)

3) Outputs {O} (e.g. formation, tray water, steam etc.)

The following embodiments present an analysis methodology which is basedon the statistical analysis of collected parameter values (i.e. data).As discussed below, in certain cases there is a correlation orrelationship between several parameters. In these cases it is oftenbeneficial to examine the impact of the interrelated parameters as acluster.

To cope with the complexity due to the multitude of variables involvedin a parameter-based process, a process model is developed. For a betterunderstanding of a process under analysis, particularly one in which thenumber of parameter values and their sub values is too large for easydistribution into areas and sub-areas, the parameters may be organizedinto a Knowledge Tree (KT) with multiple cells and multiple layers. TheKT model represents a breakdown of a process into intermediate processstages. The outputs of the lower intermediate stages become the inputs,or parameter values, of the stage above it in the hierarchy of the KT.In practice, such cells and layers are created using expert advice inorder to break down the process into appropriate stages according to itsphysical and/or logical aspects, each stage being represented by a cellor a layer. Experts determine which of the parameters are relevant in aparticular process or stage of a process. Models are then created forthese cells that describe the relationship between the inputs and theoutputs of the individual cells. As the process proceeds, newly measureddata may validate the real influence that each parameter has on theprocess.

FIG. 1 shows a KT model of a paper manufacturing process. The papermanufacturing process includes three stages: mixing, wet and dry.Controllable parameters such as pH and chalk are indicated with verticalarrows, whereas measurable parameters such as flow and conductivity areindicated with horizontal arrows. Note that the controllability of avariable may be time dependent. For instance pH in the ‘Mixing’ stage iscontrollable but then it is determined and at the Wet Stage it isuncontrollable. The uncontrollable arrow leading from the Mixing stageto the Wet stage is a variable (measured or calculated) expressing theMixing risk or quality. Similarly, the uncontrollable arrow leading fromthe Wet stage to the Dry Stage is a variable (measured or calculated)expressing the Wet Stage risk.

It is assumed that there is a causal relationship between the processconditions (i.e. the values of the process parameters) and theoccurrence of a fault. In other words, it is expected that a fault ismore likely to occur under certain process conditions. Identifying andavoiding such process conditions may reduce the occurrence of fault.

Reference is now made to FIG. 2, which illustrates a timeline for faultoccurrences. FIG. 2 shows the occurrence of three faults (PT). Eachfault is preceded by a pre-fault (pre-PT) period of length T. The timeperiods between the pre-PT/fault occurrences are considered to be“normal” operation. It is assumed that the conditions (i.e. parametervalues) during the pre-PT period resulted in an increased probabilityfor the occurrence of a fault, relative to the parameter values duringnormal operations.

Reference is now made to FIG. 3, which is a simplified block diagram ofan apparatus for the analysis of a process having parameter-basedfaults, according to a preferred embodiment of the present invention.Apparatus 300 (denoted herein a process analyzer) includes parametervalue inputter 310, fault detector 320, learning file creator 330 andlearning file analyzer 340. Parameter value inputter 310 inputs thevalue of one or more process parameters, which preferably include atleast one controllable process parameter. The parameter valuespreferably undergo an integrity check, and are stored or buffered forfurther processing. Fault detector 320 detects the occurrence of processfaults. Process fault detection may be performed automatically, eitherby examining the input parameter values or by receiving an indicationfrom a sensor or other unit. Alternately, fault detector 320 may receiveinput from a user indicating that a fault has occurred. Fault detectionis correlated with the timeline of the incoming data, so that it ispossible to know when the input values were obtained relative to theoccurrence of the fault.

Learning file creator 320 separates the buffered input values into twolearning files. The first learning file (denoted the Fault LF) containsinput process parameter values from a collection period preceding eachof the detected faults. The second learning file (denoted the Normal LF)includes data collected outside the collection periods, which isexpected to represent “normal” process conditions over a time intervalthat is longer than T. In the following, parameter values/functionsrelated to the pre-fault zone are marked by (+), and those related tothe normal zone are marked by (−).

Learning file analyzer 340 performs a separate statistical analysis ofFault and Normal LFs. These statistical analyses may be used to assessthe process status, as described below. In the preferred embodiment theprocess status assessment includes an estimated. Pre-fault time or/andestimated probability of the occurrence of a fault within a given timeinterval, based on a specified (or current) set of parameter values. Inother words, the statistical analyses are used to identify parametervalues, or constellations of values, which indicate a high probabilityof an imminent fault. Preferably, the statistical analysis consists ofcalculating a joint probability density function for the processparameters.

In the preferred embodiment process analyzer 300 evaluates a processrisk level on the basis of the separate statistical analyses performedby learning file analyzer 340. The risk evaluation process is describedin detail below. In brief, learning file analyzer 340 converts the FaultLF into a first probability density of occurrence and the Normal LF intoa second probability density of occurrence, for pairs (or largerclusters) of parameters. Learning file analyzer 340 then superimposesthe two probability densities of occurrence onto a single graph (seeFIG. 6 below), and determines a fault risk level over the graph based onthe superimposed probability densities. In a further preferredembodiment, learning file analyzer 340 calculates an overall processrisk by interpolating fault risk levels over multiple graphs.

In the preferred embodiment, process analyzer 300 further includesprocess modeler 350, which provides a model of the process. Processmodeler 350 preferably models the process as a knowledge tree mapping ofrelationships between the process parameters and process outputs. TheKnowledge Tree (KT) for system is built in cooperation with the expertsand/or operators, and identifies connections between process outputswith the corresponding measurable and controllable process parameters.

In the preferred embodiment, process analyzer 300 further includesclusterer 355, which clusters interrelated process parameters. Clusterer355 preferably forms the clusters in coordination with process modeler350. The clusters jointly provide information about the likelihood of animminent process fault. In order to obtain effective results, clusterer355 determines which combinations of parameter are interrelated andshould be taken into account, and which are useless in terms ofpredictions of probabilities and assessing the process situation.

Performing a joint statistical analysis for several different parametersis a computationally complex and time-consuming process. In thepreferred embodiment the process parameters are grouped into clusters,preferably into pairs, and a statistical analysis of Pre-fault andNormal operation is performed for each cluster independently. Thus, aprocess having ten parameters may be analyzed by determining theprobability density function (PDF) of several pairs of variables thatwere determined to be representative of the process, which requires onlymoderate computing resources. Note that a single variable may appear inmore than one pair

FIG. 4 a shows an example of parameter data collected in a learningfile, for two parameters (power and microphone signal) of an unspecifiedprocess. The parameter values are grouped within a certain range ofvalues, with a small number of outlying data points (circled). FIG. 4 bshows a joint PDF for the two parameters, where a higher probabilityregion is seen over the grouped data points.

The statistical analysis performed by learning file analyzer 340 mayresult in a discontinuous distribution. Preferably, learning fileanalyzer 340 performs a smoothing operation to obtain a smoothly varyingPDF, as shown in FIGS. 5 a-5 d.

Reference is now made to FIG. 6, which is a simplified exemplary diagramof the output of learning file analyzer 340. FIG. 6 shows a pair ofoverlapping PDFs, which were generated for a clustered pair of processparameters, based on the Fault and Normal learning files. The two PDFscreate three regions of parameter values. The region labeled “Good”indicates parameter combinations which occurred only in the Normal LF. Aprocess is operating at these levels will therefore be expected to a lowprobability of imminent fault. The region labeled “Bad” indicatesparameter combinations which occurred only in the Fault LF. The regionlabeled “Indefinite” indicates parameter combinations which occurred inboth the Fault and Normal LFs, and for which it is difficult to evaluatea process status. Parameter values occurring outside the PDFs occurinfrequently, and may indicate that the process is operating outside ofrecommended limits. PCT Pat. Appl. IL2005/001132 presents a system forthe detection of rare data situations in processes, and is herebyincorporated by reference in its entirety.

In the preferred embodiment, a single risk score is assigned toclustered parameter values, based on the Fault and Normal statisticalanalyses. FIGS. 7 a-7 c illustrates how a Normal PDF (FIG. 7 a) andFault PDF (FIG. 7 b) may be combined to form a risk score distribution(FIG. 7 c) for a pair of parameters.

Preferred embodiments for statistical analysis and risk evaluation(scoring) are now presented.

As discussed above, a user initially sets the duration of the pre-faultperiod T based on the user's expertise and knowledge of the process, andthe collection period is set to a time interval that is shorter than T.The T value may be set by the user to be the same for all the processparameters, or to be specific for a particular parameter group (cluster)(T=T_(i)). A criterion is introduced below that enables thedetermination of an optimal value of (T=T_(i)) in order to increase thesystem's resolution.

In the preferred embodiment, file analyzer 340 performs a separatestatistical analysis for each two-parameter cluster (x,y). Two PDFs arebuilt, respectively ρ₊(x,y) and ρ⁻(x,y), on the basis of the Fault andNormal Learning Files.

The unconditional probability of the process being in the Pre-faultstatus is calculated as:

-   -   When T has been set uniform for all the clusters,

p ₊ =nT/ΘN ₊/(N ₊ +N ⁻)

When T is cluster specific,

p _(+i) =nT _(i)/Θ

where n is number of faults, Θ is the overall duration of the learningperiod, N₊, N⁻ are the sizes of the Fault and the Normal learning filesrespectively (the size being the number of elements such as points,records etc. constituting the LF).

Using the Bayes formula, we obtain:

p ₊|_(x,y) =p ₊(x,y)p ₊/(ρ₊(x,y)p ₊+ρ⁻(x,y)(1−p ₊)),

where p₊|_(x,y) is a conditional probability of the process being in thePre-fault status given the values of x and y.

On the basis of the formula, two functions are introduced:

1. Ratio Function ψ—

ψ=p ₊|_(x,y) /p ₊=ρ₊(x,y)/(ρ₊(x,y)p ₊+ρ⁻(x,y)(1−p ₊)),

defines the relation between the conditional and the unconditionalprobabilities of the process being in Pre-fault, i.e. prediction of theprobability of a fault within time interval T.

2. Sample Size Function φ

φ=ρ₊(x,y)+ρ⁻(x,y),

assesses to what extent the ratio function prediction is well grounded.

The following example illustrates the necessity of the two functions.When estimating the probability of high company revenue, we may receivea high value of ψ. However, if this figure is obtained over insufficientstatistic sample, the φ will be low, signaling that the prediction maybe not reliable.

In the present case, the smaller the ψ value, the better the processstatus is in relation to fault conditions, i.e. the probability of afault appears small. However, if the φ value is small, the currentparameters occur rarely and the process status may be poor. We do notknow what exactly the problem is, but we know that our optimisticprediction is not reliable enough.

Preferably, instead of the two ψ and φ functions, a single combinedformula may be used which provides a more efficient combined estimationof the p₊|_(x,y)/p₊ relationship:

Φ(x,y)=ψ(x,y)(1+ε/φ(x,y))

where ε is an optional (preferably small-valued) parameter defined for aspecific process model.

A cluster-specific function, Φ_(i), is also introduced. Φ_(i) expressesthe conditional probabilities of the process being in Pre-fault statusfrom the point of view of a given cluster. The function Φ_(i) may beused to rate the efficacy of the process operator decisions from thepoint of view of a particular cluster.

A Φ chart which graphically represents the value of Φ of as a functionof the parameters (x,y) may be generated and provided to the processoperator. The Φ chart may prove useful to the process operator byshowing preferable combinations of the process parameters (x,y) in orderto avoid the fault.

The function Φ_(i)(x_(i),y_(i)) may be used to estimate the average timebefore fault from the point of view of specific parameter values of aparticular cluster i:

Tav _(i) =T _(i)/(2p _(+i)Φ_(i)(x _(i) ,y _(i)))

If the probability of a fault is low for a certain cluster, thecluster-related T_(av) value will be high. Likewise, a low value ofT_(av) indicates a high probability of a fault.

The function Φ_(i) estimates the steadiness of the current processsituation in relation to fault occurrence, the efficiency of the ProcessOperator decisions etc, from the point of view of a particular cluster.A criterion is now described which enables informative clusterselection.

A cluster (x_(i),y_(i)) is informative for only if its role in theprocess influences the fault probability of occurrence. That is:

-   -   Areas in the parameter space of x_(i),y_(i) where Φ_(i)>0 or        Φ_(i)<0    -   Those areas are populated with points from a LF

A preferred embodiment of a cluster selection criterion that meets theabove-formulated requirements is based on:

C=the value of (Φ_(i)(x,y)−1)² averaged over the Fault LF and the NormalLF 2.

The user sets the range of C values for cluster selection (e.g., thefirst five clusters with C above a given value).

Until now we have selected a number of relevant clusters and collectedtheir particular ‘assessment’ of the risk level for a given cluster. Afinal decision may then be formulated on the overall process status,based on a generalization of the individual cluster risk levels.

In the preferred embodiment, process analyzer 300 further includes riskevaluator 360, which evaluates a fault risk level for a processoperating at a specified set of process parameter values. In thepreferred embodiment the fault risk level is evaluated as an estimatedtime until fault and/or an estimated probability of a fault within aspecified time interval. The fault risk level may be determined asdescribed below.

Suppose that there are K clusters with C values C₁, . . . , C_(K) thatare within the range of relevance. These clusters have Φ functionsΦ₁(x₁,y₁), . . . , Φ_(k)(x_(k),y_(k)). The corresponding averagePre-fault times are:

Tav ₁ =T ₁/(2p _(+,1)Φ₁(x ₁ ,y ₁)), . . . , Tav _(k) =T _(k)/(2p_(+,k)Φ_(k)(x _(k) ,y _(k)))

A model is built for predicting the time until fault (i.e. the predictedPre-fault time) as based on the per-cluster average Pre-fault timesTav₁:

τ=F(Tav ₁ , . . . , Tav _(k))

τ is the predicted Pre-fault time and Tav_(i) is the estimated Pre-faulttime for a particular cluster. The model may be based on expertjudgment, averaging, simple regression, and other factors.

The probability of a fault within time interval T under given conditionsmay be estimated from τ as follows:

p ₊ ^(CONDITIONAL) =T/2τ

This probability may be more valuable for the client than the estimatedaverage Pre-fault time.

Thus the risk level for parameter values {(x₁,y₁), . . . ,Φ_(k)(x_(k),y_(k))}, may be given as one or a combination of τ and p₊^(CONDITONAL).

Preferably, process analyzer 300 serves to monitor an ongoing process,in which case the risk evaluator operates on the current parametervalues. When process analyzer 300 identifies that the process isoperating with parameter values which indicate a high probability forthe occurrence a fault (i.e. a high risk level), the operator may bealerted and provided with information to assist the operator to avoidthe upcoming process fault.

Preferably, when the predicted Pre-fault time, τ, is smaller than thepresent average value, T_(av), the process operator is provided with:

-   -   Alarm    -   The estimated time until fault T or/and estimated probability of        PT within a given time interval p₊ ^(CONDITIONAL)    -   Φ-charts for relevant clusters with marked current value points

On the basis of the information provided by the system, the processoperator may choose to change the process parameters or materials(“recipe”). For an on-line process, the operator may be provided withfeedback showing the impact of his latest decision on the processbehavior. The feedback information preferably includes:

-   -   The impact of the recipe changes on the predicted Pre-fault time        τ or/and estimated probability of fault within a given time        interval p₊ ^(CONDITIONAL)    -   The trajectories of the parameter values after the recipe        change, against the background of the relevant clusters.        In this way, the process operator may benefit from a continuous        on-line assessment of the changes made in the process recipe,        thereby providing the basis for the next decisions to be made.

In the preferred embodiment, process analyzer 300 further includescontrol unit 390 which determines preferred values for one or more ofthe controllable parameters, so as to reduce a probability of a fault.

The defined function F expresses the dependence of the predictedPre-fault time on the basis of the inputs. Control unit 390 preferablyfinds the values of controllable parameters that maximize F value, andthen proposes these values to the process operator as the recommendedoptimum recipe.

A difficulty arises when F depends not only on process inputs but alsoon certain outputs {O} that in turn depend on controllable values {C},and which will therefore be changed when {C} values change. In suchsituations, in order to be able to perform optimization (i.e. tomaximize F values) it is necessary to build predictive models using a KTand possibly a design of experiments (DOE). The DOE approach conductsand analyzes controlled tests to evaluate the factors that control thevalue of a parameter or group of parameters, and provides a causalpredictive model showing the importance of all parameters and theirinteractions.

The optimization task may be formulated as:

F({Φ({M},{C},{O _(predicted)({M},{C}}})→max by {C}

Control unit 390 performs the above optimization, and provides real-timerecommendations for controllable parameter values (recipes) that areexpected to reduce the likelihood of a fault. The recommendation may bebased on the past data records of a unit, such as a production machine,that has controllable and uncontrollable (measurable) process variablesas well as fault event records.

Control unit 390 preferably performs ‘dynamic targeting’ on theproduction process, whereby intermediate targets like mixing risk may bemodified due to failures in outcomes of previous recipes. For example,in the paper-making process of FIG. 1, if the value of the controlledvariable pH was different from its intended target value (Mixing risk),its value is considered uncontrolled in ‘Wet Stage’ and the target valueof ‘Dry Stage’ risk will be adjusted so that the final target (Breakrisk) is held intact or minimized. U.S. Pat. Nos. 6,728,587 and6,678,668 address the issue of dynamic targeting, and are herebyincorporated by reference in their entirety.

In the preferred embodiment, process analyzer 300 includes a discretizerwhich divides a range parameter values into discrete sub-ranges.Preferably, the discretizer selects split values for the sub-rangeswhich maximize an average time between faults while retaining astatistically significant sample within a range, as shown in FIG. 8 a.

A preferred embodiment for selecting split values for a selectedcontrollable process parameter is performed as follows.

1) Divide the measurable (uncontrollable) parameter values intosub-ranges. Any combination of measurable parameter values may thus beclassified as a discrete vector.

2) Based on collected parameter value data, generate a record subset ofall past records with the common uncontrollable vector.

3) For each controllable parameter, generate a histogram of the lengthof time that the process was at various controllable parameter values.

4) Find a value X_(n) of the controllable parameter such that theaverage time between faults T_(n) is as large as possible (on one side)while keeping the number of observations N statistically significant. Xnis a controllable parameter split value that yields a ‘good’ splitbetween a high fault risk and a low fault risk with statisticalsignificance.

FIG. 8 b shows an example of a histogram for chalk levels in a papermaking process. Paper break frequency was 3.1 times higher inobservations where chalk levels were less than 239. About 16% ofobservations were in this category. A chalk level of 239 is therefore agood split level for this parameter.

The above-described embodiments perform statistical analyses ofcollected process parameter value data. The evaluation of a process risklevel from these statistical analyses is a computationally complex task,even when parameters are clustered together, so that process riskevaluations may be performed on a per-cluster basis. In the preferredembodiment discussed below, historical process parameter data isanalyzed to compile a multi-dimensional array of discretized processparameter values. The array then serves as a lookup table, forestimating the risk level for a current (or otherwise specified)constellation of parameter values.

In the preferred embodiment, risk evaluator 360 includes parameter arraygenerator 370 which maintains a risk level array for the process. Eachdimension of the array is associated with a respective processparameter, which is subdivided into sub-ranges (see below). A risk levelis determined for each element of the array, based at least in part onthe Normal and Fault PDFs. The risk level is preferably determined froma historical distribution of the risk level for each element of thearray, as determined over time by risk evaluator 360.

In a further preferred embodiment, the risk level is derived fromPre-fault time τ or/and estimated probability of fault within a giventime interval p₊ ^(CONDITIONAL)

FIG. 9 a shows a set of six process parameters, of which four aremeasurable and two are controllable. FIG. 9 b shows a historical risklevel distribution for four individual vectors (CCABCC, CCABCA, CCABBCand CCABAB), and for the overall set of vectors. Vectors CCABCC andCCABAB are considered high-risk vectors, vector CCABBC is considered amedium risk vector and vector CCABCA is considered a low risk vector.

The risk level array serves as a lookup table for risk evaluator 360. Asa current set of parameter values is read in by parameter value inputter310, the corresponding risk level is obtained from the risk level array.An ongoing process may thus be monitored with a minimal investment ofcomputational resources, even when the process involves a large numberof parameters.

In the preferred embodiment the risk level is a binary indicator, whichindicates high-risk and low-risk process conditions. An alert may beprovided to the process operator whenever a high-risk indicator occurs,when a high-risk indicator occurs with a certain frequency, or accordingto specified alert rules.

As discussed above, errors may occur when the statistical sample uponwhich an analysis is based is inadequate. Combining the double-PDFanalysis described above with the risk level array enables determiningthe current process status with a greater level of confidence.

Reference is now made to FIG. 10, which shows a Normal and a Fault PDFsuperimposed over a two dimensional risk level array. The graph visuallyprovides the operator with the risk level estimates of both the dual-PDFand the risk level array. As discussed for FIG. 6 above, the dual-PDFsindicates three regions, which correspond to Good, Bad, and Indefiniterisk estimates. In addition, each quadrant of the graph visuallyindicates the risk level given by the corresponding entry in the risklevel array (for example, each quandrant may be colored to reflect therisk level array estimate). The process operator may thus compare bothestimates when analyzing the current process status.

Reference is now made to FIG. 11, which is a simplified block diagram ofa system for the monitoring and control of a parameter-based industrialprocess, according to an exemplary embodiment of the present invention.Monitor unit 1110 obtains parameter value data from database (DB) 1120and Supervisory Control and Data Acquisition (SCADA) unit 1130, andperforms dual-PDF 1140 and risk level array analyses (denoted “Red RoadVector”) 1150 upon the data. Alerts and reports are then generated byreport unit 1160, and provided to control room operators, engineers andmanagement personnel.

Reference is now made to FIG. 12, which is a simplified flowchart of amethod for the analysis of a process having parameter-based faults, inaccordance with a preferred embodiment of the present invention. In step1210, the values of at least one process parameter are input. Theparameter values are recorded over a time period, which preferablyincludes multiple faults and intermediate normal periods to allow for astatistically significant collection of data.

In step 1220, process fault occurrences are detected. Based on thedetected process faults, two learning files are created in step 1230.The learning files consist of a Fault LF which includes parameter valuesfrom a collection period preceding each of said detected faults, and aNormal LF which includes parameter values from normal periods (i.e. doesnot include data Pre-fault periods).

In step 1230, a separate statistical analysis of the two learning filesis performed as described above. The statistical analyses may serve toassess the process status for a specified set of parameter values.Preferably the statistical analyses are performed separately for definedclusters of parameters. Preferably, the statistical analysis provides adual-PDF statistical analysis of the parameter data.

Preferably, the method includes the further steps of evaluating processrisk and/or monitoring an ongoing process and/or providing user alertsand information (indicated jointly as step 1250). Evaluating processrisk preferably includes correlating a dual-PDF statistical analysiswith a risk level array, as described for FIG. 10.

In the preferred embodiment a process risk level is determined byconverting the first and second learning files into separate probabilitydensities of occurrence, for at pairs (or larger clusters) ofparameters. The probability densities are then superimposed onto asingle graph, and a fault risk level is determined over the graph withrespect to the superimposed probability densities.

Preferably, the method includes the further step of interpolating faultrisk levels determined over a plurality of graphs to obtain an overallrisk.

In summary, a knowledge tree maps the process stages (such as themixing, wet and dry stages of the paper making process). The dual-PDF(Normal/Fault) methodology provides a unique estimation of the processfault risk. The risk level array methodology delivers recommendationsbased on past history, which are correlated with the results of thedual-PDF analysis. Key variables, the associated risk and therecommendations may be visualized in a simple graphical user interface(GUI).

The above-described embodiments present a highly effective apparatus andmethod for analyzing parameter-based faults in a process, for monitoringan ongoing process, and for reducing the frequency of such faults. Inpaper manufacturing, and in many other manufacturing processes, even asmall decrease of faults may reduce production costs significantly.

It is expected that during the life of this patent many relevantparameter-based processes, manufacturing and industrial processes, andstatistical analysis methods will be developed and the scope of thecorresponding terms is intended to include all such new technologies apriori.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub combination.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims. All publications, patents and patentapplications mentioned in this specification are herein incorporated intheir entirety by reference into the specification, to the same extentas if each individual publication, patent or patent application wasspecifically and individually indicated to be incorporated herein byreference. In addition, citation or identification of any reference inthis application shall not be construed as an admission that suchreference is available as prior art to the present invention.

1. An apparatus for the analysis of a process having parameter-basedfaults, comprising: a parameter value inputter configured for inputtingvalues of at least two process parameters; a learning file creatorassociated with said parameter value inputter, configured for generatinga set of said input values obtained during normal process behavior andfor creating a learning file for said set of input values, wherein saidlearning file is arranged for joint statistical analysis of said atleast two process parameters; a learning file analyzer associated withsaid learning file creator, configured for performing a statisticalanalysis of said learning file so as to detect constellations ofparameter values indicative of a fault, thereby to assess a processstatus; and process monitor associated with said learning file analyzer,configured for monitoring parameter values of an ongoing process so asto determine a current status of said ongoing process, and to providealerts to a process operator in accordance with said current processstatus.
 2. The apparatus of claim 1, wherein said learning file analyzeris configured generate a model of a statistical distribution of saidinput values during normal process behavior.
 3. The apparatus of claim2, wherein said model comprises a joint probability density ofoccurrence of constellations of parameter values during said normalprocess behavior.
 4. The apparatus of claim 3, wherein said modelspecifies regions of parameter values in accordance with saidprobability density of occurrence, each of said regions being associatedwith a respective process status.
 5. The apparatus of claim 4, whereinsaid process monitor determines said current status from a location ofcurrent parameter values within said regions.
 6. The apparatus of claim4, wherein said process monitor is configured to provide an alert whensaid current status comprises a deviation from a region of normalprocess behavior specified by said model.
 7. The apparatus of claim 4,wherein said process monitor is configured to determine said currentstatus from a trajectory of said input parameter values over said model.8. The apparatus of claim 7, process monitor is configured to provide analert when said trajectory approaches a deviation from a region ofnormal process behavior specified by said model.
 9. The apparatus ofclaim 1, wherein said learning file analyzer is configured to performjoint statistical analyses of pairs of parameters.
 10. The apparatus ofclaim 9, wherein said joint statistical analysis comprises providing amulti-dimensional probability density function of said pairs ofparameters.
 11. The apparatus of claim 1, further comprising a controlunit configured to determine a preferred value of a controllableparameter so as to reduce a probability of a fault.
 12. The apparatus ofclaim 11, wherein said control unit is further configured toautomatically adjust a value of said controllable parameter to saidpreferred value.
 13. A method for the analysis of a process havingparameter-based faults, comprising: inputting the values of at least twoprocess parameters over time; generating a set of said input valuesobtained during normal process behavior; creating a learning file forsaid set of input values, wherein said learning file is arranged forjoint statistical analysis of said at least two process parameters;performing a statistical analysis of said learning file so as to detectconstellations of parameter values indicative of a fault, thereby toassess a process status; monitoring parameter values of an ongoingprocess so as to determine a current status of said ongoing process; andproviding alerts to a process operator in accordance with said currentprocess status.
 14. The method of claim 13, further comprisinggenerating a model of a statistical distribution of said input valuesduring normal process behavior.
 15. The method of claim 14, wherein saidmodel comprises a joint probability density of occurrence ofconstellations of parameter values during said normal process behavior.16. The method of claim 15, wherein said model specifies regions ofparameter values in accordance with said probability density ofoccurrence, each of said regions being associated with a respectiveprocess status.
 17. The method of claim 16, further comprisingdetermining said current status from a location of current parametervalues within said regions.
 18. The method of claim 16, wherein an alertis provided when said current status comprises a deviation from a regionof normal process behavior specified by said model.
 19. The method ofclaim 16, further comprising determining said current status from atrajectory of said input parameter values over said model.
 20. Themethod of claim 19, wherein an alert is provided when said trajectoryapproaches a deviation from a region of normal process behaviorspecified by said model region.
 21. The method of claim 13, wherein saidprocess status comprises at least one of a group consisting of: anestimated time until fault, and an estimated probability of a faultwithin a specified time interval.
 22. The method of claim 13, furthercomprising determining a preferred value for a controllable parameter soas to reduce a probability of a fault.
 23. The method of claim 22,further comprising adjusting said controllable parameter to saidpreferred value.